Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Optimisation of a storage hydrogen system under liquid form

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire de Nanocaractérisation et Nanosécurité

01-10-2019

SL-DRT-19-0586

vincent.faucheux@cea.fr

Hydrogen could represent the best way to transport energy as long as it is produced from renewables energies and stored/transported safely and at low cost. A way to fix hydrogen consists of using organic molecules such as LOHC (Liquid Organic Hydrogen Carrier). Indeed, LOHC have H2 contents à room and pressure temperatures which can be upper than for H2 under pressure. These unsaturated molecules directly react with H2 in the presence of a catalyst. Thus, the transit of H2 consist simply in the transport of a liquid. Dehydrogenation is then realized where the hydrogen is needed. Nevertheless, the energetic cost to dehydrogenize these molecules constitute the lock to the emergence of this technology. The goal of this thesis consist to determine the best triplet LOHC molecule/catalyst/catalyst support and to improve the interfaces LOHC molecule/catalyst and catalyst/catalyst support to decrease the reaction temperature, improve the heat and mass (liquid-gas) transfers, improve the dehydrogenation kinetics, and at the end, decrease the energy necessary to the reaction. This work imply from a modelling step, in the synthesis of a molecule (or its choice) with all the criteria of performances, the determination of an optimum catalytic support in term of porosity, thermal and electrical conductivity (from a thermo-fluidic modelling), the choice of a catalyst (bibliographic study) and its deposition, and then the validation of the models and subsets through experimental measurements. This optimisation will include the realization of a catalytic system (catalyst and support) in relation with a LOHC molecule.

3D reconstruction of nanometric objects from stereoscopic electron microscope images

Département Technologies Silicium (LETI)

Laboratoire

01-10-2019

SL-DRT-19-0588

aurelien.fay@cea.fr

Keywords : Images treatment, GPU programmation, optimisation, inverse problem, stereovision, neural networks. Robust, non-destructive and fast 3D metrology is a world-wide major challenge of microelectronics industry for defects inspection, optical lithography fidelity and process control. Fast reconstruction methods from stereoscopic electron microscope (SEM) images based on geometrical considerations allow to reconstruct 3D topography of micronic objects. However, those technics cannot be applied on nanometric objects because of local physical phenomena which disturb the placement of the points of interest [2]. Alternative methods based on the resolution of inverse problem have been already prototyped. Significant improvements of the computation time are expected after their implementation on the GPUs of our group. Model calibration methods must also be developed, potentially based on neural networks. 3D metrology based on SEM images arouses the interest of several LETI industrial partners, and this thesis is intended to be a key element for present and future collaborations in this field. The objective of this thesis is to develop a 3D metrology from SEM images the most precise and robust as possible. For this, the PhD student will initially use the group's theoretical and simulation resources to improve and develop new SEM imaging models. The scope of these models is broad, from the simulation of micrometric objects to nanometric structures. CEA-LETI has a new generation of SEM that allows to image patterns at different points of view. These multi-stereo images allow an increase of data on the image structure which facilitates its 3D reconstruction, compared to the case of a single SEM image taken in top view. The PhD student will train the SEM models with a collection of stereoscopic SEM images of patterns, which 3D topographies will be known from reference 3D metrology. The student will investigate, in a second time, different mathematical strategies for the 3D reconstruction, allowing fast and precise convergence. Eventually, 3D reconstruction will be applied on different customer products of interest.

Compressed Sensing for elastic guided wave tomography applied to Structural Health Monitoring

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Méthodes CND

01-10-2019

SL-DRT-19-0591

tom.druet@cea.fr

Structural Health Monitoring (SHM) relies on the permanent integration of sensors to continuously inspect the integrity of industrial structures. In SHM, Guided Waves (GW) allow large area monitoring due to low attenuation coefficients and high defect sensitivity. Applications of SHM include the detection of corrosion in metals in the oil & gas and nuclear industries, as well as the detection of delamination in composite materials in aeronautics. Guided Wave tomography is one approach to conduct monitoring over time of a region of interest and lead to an accurate 3D visualization of the inspected region providing detection, localization and quantification of defects. The performances of GW tomography algorithms highly depends on the number of sensors used for the inspection. Currently the number of sensors required may be prohibitive in some industries because it leads to additional wiring, complexity and added mass. One approach to reduce significantly the number of sensors is to use Compressed Sensing (CS). CS is a group of signal processing techniques to sample signals below the traditional Nyquist-Shannon sampling theorem. To do this, CS relies on two fundamental components: the measurements must be incoherent and the signal to reconstruct must have a sparse description. In GW tomography, this translates to mathematically reformulate the reconstruction algorithms to reveal these properties during the resolution process. As in SHM, sensors are permanently bounded and in CS the measurement points must be incoherent, a major task will be to optimize sensor positioning. The use of CS in GW tomography is expected to reduce the number of sensors required by at least a factor of 2. The objectives of this thesis are the following: 1) development of the GW tomography algorithm using CS in the reconstruction step with a specific sensor positioning methodology; 2) optimization and automation of the reconstruction process; 3) Implementation of the methodology on numerical and experimental data. This thesis is the result of a collaboration between the NDT department of the CEA and the Neurospin institute, also at CEA. While the first has made significant development in GW tomography the second has made extensive contributions in the field of CS.

Study of processes involving dense fluid for a circular economy with low environmental impact in the photovoltaic field.

Département des Technologies Solaires (LITEN)

Laboratoire Matériaux et Procédés Silicium

01-10-2019

SL-DRT-19-0594

claire.audoin@cea.fr

The photovoltaic industry (PV) generates a large volume of wastes. In addition to production waste (ingot chunk, kerf-loss powder, silicon scrap, etc.), increasing quantities of end-of-life PV panels will have to be treated by 2030. Considered since 2012 as WEEE waste, it is crucial to develop recycling processes. The processes currently used are essentially mechanical processes that primarily promote the recycling of glass and aluminum frame. The recovery of more critical materials such as silicon, silver, copper ? would give an attractive added values for stakeholder in the recycling field. One of the major barriers in the recovery of these materials is the elimination or the delamination of the encapsulation polymer layer (EVA) to allow full separation of the different layers constituting a PV panels (Glass/EVA/Si-Cells/EVA/Backsheet). To that end, some chemical and thermal processes exist in order to remove the EVA layer. However, these methods remain solutions that are not very respectful of the environment. They produce more or less significant levels of hazardous gaseous or liquid effluents. The challenge is to provide solutions with low environmental impact and economically viable. In this context, two CEA laboratories, the LPSD (DEN) and the LMPS (DRT) have carried out feasibility studies of a treatment process involving one or more non-polluting fluids under subcritical (SubC) or supercritical (SC) like CO2 and water for recycling of PV modules. This method involves little known diffusional and interaction mechanisms with the multilayer structure. The understanding of these mechanisms will eventually define the parameters applicable to the recycling process of PV panels to allow recovery of valuables materials (glass, Si, Ag, for example?.). The aim of the PhD is to explore the potential of processes using supercritical fluids under unconventional conditions for the realization of the different key steps in the treatment of PV panels: delamination and extraction of metals of interest. To understand these mechanisms, the candidate will have the opportunity: to design and make specifics samples, to implement treatments in supercritical and/or subcritical fluids as well as complex systems, to rely on advanced physico-chemical characterizations of surfaces and interfaces.

Advanced photoemission of buried critical interfaces in micro-electronics of power and memory devices

Département Technologies Silicium (LETI)

Autre laboratoire

01-10-2019

SL-DRT-19-0596

orenault@cea.fr

Nano-devices down-scaling requires a more and more severe control of buried interfaces of nano-layers underneath a top electrode. To this end, there is a pressing need for developing innovative physical characterization methods having a strong non-invasive character and a high degree of precision for direct transfer to in-line metrology. The context here is the consolidation and generalization of a generic method based on X-ray Photoelectron Spectroscopy and hard X-ray Photoelectron Spectroscopy and using inelastic losses to provide elemental depth distribution at the nanoscale (Tougaard's method), and its complementarity with well-established ARXPS method. The local character of the analysis is provided either by an X-Ray micro-source, or by an XPS microscope. The objectives will be, on the one hand, to clarify the limits of the approach within the multi-material applicative framework of power, memory and photonic device; on the other hand the challenge is to consolidate the existing background modelling techniques in order to reach high degrees of precision by generalizing the most recent approaches (customized inelastic cross-sections, reference samples, several layer models, extensive use of hard X-ray probes.

CNN-3d-lensfree

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Imagerie et Systèmes d'Acquisition

01-09-2019

SL-DRT-19-0605

lionel.herve@cea.fr

At CEA-Leti, we are developing lensfree microscopy for the monitoring of cell culture. This technique overpass several limits of conventional microscopy (compactness, field of view, quantification, etc.). Recently we showed, for the first time, 3D+time acquisitions of 3D cell culture with a lens-free microscope. We observed cells without any labelling within the volume as large as several cubic millimeters over several days. This new mean of microscopy allowed us to observe a broad range of phenomena only present in 3D environments. However, two drawbacks are still present on the microscope prototype: a long reconstruction time (>1 hour/frame) and the reconstructed volumes present artefacts owing to the limited number of angular acquisitions. The thesis work will focus on the ability of deep learning technologies to overcome the above-mentioned limitations. Basically, a convolutional neural network will be trained on the basis of simulated 3D cell culture volume (ground truth) and simulated response of our current 3D lensfree microscope (input). This approach is expected to accelerate the reconstruction process and to allow full 3D reconstructions. Yet it poses two scientific questions: are simulated data pertinent to train a neural network and how can we assess the quality of 3D reconstruction obtained through a neural network? Profile of the candidate sought: - Engineering degree in applied mathematics or physical sciences. - Strong knowledge in image processing with skills in deep learning.

134 Results found (Page 9 of 23)
first   previous  7 - 8 - 9 - 10 - 11  next   last

Voir toutes nos offres